Soybean Genomics Research Program Strategic Plan

This strategic plan builds on the soybean communities’ previous efforts (October, 1999; July, 2001; May, 2003; July, 2005; and May, 2007) to review progress on the development and deployment of soybean genomic resources. The results are impressive (see Soybean Genomics Research Program Accomplishments Report, 2010, available at http: //soybase. org/SoyGenStrat2007/SoyGenStratPlan2008-2012-Accomplishments%20v1. 6. pdf verified 17 Mar. 2011). For example, in the last 5 yr the soybean research community has produced a genetic linkage map with over 5500 mapped markers spanning the entire 2296 cM soybean genome. A set of 1536 SNP markers that are evenly distributed across the 20 linkage groups was developed for whole genome analysis of polymorphisms in both elite North American cultivars and breeding lines. In addition, an expanded array of 50, 000 SNPs is under development which will be used to create haplotype maps of over 18, 000 accessions of the USDA soybean germplasm collection. This research is scheduled for completion in late 2010 and the SNP haplotype map of each accession will be placed on the HapMap Browser on SoyBase. Large-scale shotgun sequencing of the soybean cultivar Williams 82 was completed late in 2008 by the U. S. Department of Energy Joint Genome Institute (DOE-JGI) and recently reported in the scientific journal Nature (Schmutz et al. , 2010). The present soybean assembly (Glyma. 1. 01) captured approximately 975 Mbp of its 1100 Mbp genome. The gene set integrates ∼1. 6 million ESTs with homology and predicts 66, 153 protein-coding loci available at http: //www. phytozome. net/soybean (verified 17 Mar. 2011). Soybean researchers have developed several microarray technologies for gene expression studies. The GeneChip Soybean Genome Array is commercially available for studying gene expression (http: //www. affymetrix. com/productsₛervices/arrays/specific/soybean. affx#1₁ verified 17 Mar. 2011). This GeneChip contains 37, 500 Glycine max transcripts, 15, 800 Phytophthora sojae transcripts, and 7500 Heterodera glycines transcripts. The achievement of milestones in previous strategic plans for soybean genomic research have advanced soybean to its current status as a crop model for translational genomics. Simply stated, soybean genomic resources in hand will accelerate the ability of plant breeders to enhance soybean productivity, pest resistance, and nutritional quality. However, many secrets of the soybean genome have yet to be revealed. To continue to make informed decisions it was critical to capture the consensus wisdom of leading soybean researchers on the next logical steps in the development and utilization of soybean's genomic resources. On 27–28 July 2010 Roger Boerma chaired a workshop sponsored by the United Soybean Board in St. Louis MO that brought together 44 eminent soybean researchers in the areas of genomic sequencing, gene function, transformation/transgenics, and translational genomics. The purpose of the Workshop was to develop a strategy for achieving the critical soybean genomic resources and information required to accelerate the rate of yield gain and addition of value to U. S. soybean cultivars. A consensus was reached on a number of high priority performance measures or research objectives. In addition the anticipated outcomes of successfully achieving these performance measures are included in the final plan. Overall, two issues emerged as being critically important or overarching issues: (i) Provide additional support staff for continued development and population of SoyBase, and (ii) Development of a genetic repository/distribution center for soybean mutants/transgenic lines. The enhancement of SoyBase was deemed important for all four Strategic Goals. The genetic repository/distribution center was broadly supported by Workshop participants. Listed below is an outline of the four Strategic Goals and their respective Performance Measures. Within each Goal, the Performance Measures are listed in order of importance. 1. 1: Ensure the accuracy of reference sequence assembly. 1. 2: Capturing and leveraging existing genetic diversity in soybean germplasm. 1. 3: Improving bioinformatic resources for genomic analysis and practical applications. 1. 4: Reveal function of targeted genome sequences to facilitate gene discovery and application. 1. 5: Leveraging genomic information from Phaseoloids and other species. 1. 6: Determine the role of epigenetics in soybean improvement. 2. 1: Develop comprehensive gene expression data for soybean. 2. 2: Develop near isogenic lines (NIL) to help reveal genetic mechanisms that mediate useful traits. 2. 3: Develop an improved infrastructure to facilitate genome annotation. 2. 4: Achieve high-definition genomic characterization of biological mechanisms and regulatory systems in soybean. 2. 5: Use functional genomic methods to characterize transcription regulated pathways. 2. 6: Advance gene modification technologies to help associate candidate genes with a discrete phenotype. 2. 7: Create a saturated transposon insertion population with defined flanking sequences that can be used to identify mutants by BLAST sequence comparison. 2. 8: Implement outreach opportunities for education and use of genomic databases. 2. 9: Develop an ORFeome library from agronomically important genes and gene families. 3. 1: Establish of a soybean genetic repository and distribution center. 3. 2: Develop next-generation transformation and targeting technologies and utilize these transgenic approaches to help elucidate gene function and deploy genes of interest. 4. 1: Develop analytical approaches to characterize soybean germplasm diversity based on the SoyHapMap 1. 0 data to identify parental lines for breeding purposes. 4. 2: Discover gene/QTL for qualitative traits and develop tightly linked DNA markers. 4. 3: Discover gene/QTL for quantitative traits and develop tightly linked DNA markers. 4. 4: Develop and populate a user-friendly database of validated QTL for use in marker assisted breeding applications. 4. 5: Define the molecular genetic signatures of selection in 70+ years of U. S. soybean breeding by use of the 50, 000 SNP Illumina Infinium Assay. 4. 6: Define optimum breeding models for different breeding situations using in silico analysis. The chromosome-scale draft assembly of the soybean (Glycine max L. Merr. ) genome is an outcome of a dynamic, technology driven, and timely strategic process whose origin may be traced formally to the Soybean Genomics White Paper (Boerma et al. , 2000). That January 2000 document was a product of a 21–22 Oct. 1999 meeting of 17 experts in plant genomics, DNA markers, plant transformation, and bioinformatics. A consensus was reached on research priorities in the area of soybean genomics. Milestones included: (i) doubling Simple Sequence Repeat (SSR) markers within 3 yr; (ii) expansion of Single Nucleotide markers to within 3 to 5 yr; the of soybean of the genes in the soybean genome within 3 to 5 yr; of and maps of within 3 to 5 yr; and to the and of the soybean genome. to research to the of genomic species. For the U. S. 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The purpose of the workshop was to develop a consensus strategy for achieving the critical soybean genomic resources and information required to accelerate the rate of yield gain and addition of value to U. S. soybean cultivars. This Soybean Genomics Research Program Strategic Research to Strategic Milestones the high priority Performance Measures or research and anticipated outcomes of successfully achieving Strategic Goals for soybean genomic research in the next 5 Within each Goal, the Performance Measures are listed in order of importance. Performance 1. 1: Ensure the accuracy of reference sequence assembly. is for useful of genome of the reference genome the of all of gene of gene function, genomics, and characterization of the of genetic diversity in soybean. The draft sequence of Glycine max is of high quality. However, are within the genome that may genes that are important to soybean improvement. research is to of the assembly. of in sequence of the order and of maps to and and sequence targeted to to help sequencing technologies to sequence Performance 1. 2: Capturing and leveraging existing genetic diversity in soybean germplasm. diversity is the for genetic enhancement of soybean. and other measures that of the for useful traits within the USDA germplasm of the in genetic However, in DNA sequencing technology will the high-definition characterization of germplasm and breeding lines. a of the U. S. germplasm the that of the genetic in max will genetic diversity on a to maps of the entire USDA max and to identify sequencing reference of to max lines to number genes that are present in the reference and other Performance 1. 3: Improving bioinformatic resources for genomic analysis and practical applications. a of in sequencing the of the soybean genomic and data is at are by that the ability to and data in a useful and timely is a for expansion of an database for use by all to enhance the of genome sequence for for parental selection and breeding population development and database support for of SoyBase to all information using as an For with a gene or function, a be to the of that gene on the genetic and sequence information that and to the scientific for Performance 1. 4: Reveal function of targeted genome sequences to facilitate gene discovery and application. of DNA sequences in quantitative loci the of markers for genetic traits. However, many genes may be present in a QTL of candidate gene sequences with a biological function or marker of the biological mechanisms that mediate traits in soybean. of the reference sequence is to the and of gene of entire gene to and transcripts. gene models with for accuracy of the transposon database to genes in of or that may have of regulatory that gene of for all of and of sequences from a of to the role of in gene of soybean gene to facilitate gene discovery and genetic improvement. and of functional information for all gene and of data with genetic and genomic resources in SoyBase, the soybean genetic database Performance 1. 5: Leveraging genomic information from Phaseoloids and other species. and are with to soybean. and both from Glycine and may be useful for the discovery of genes for in soybean. Genome sequencing of both is The Glycine are of of the of in Glycine as a and for the of the soybean genome. from soybean its 5 The the germplasm for and may traits as and of genetic for nutritional traits in and that may be to soybean. of the genes or quantitative loci with DNA markers to facilitate to elite cultivars. of SNP to of and and soybean and for and of with to pest and Performance 1. 6: Determine the role of epigenetics in soybean improvement. of traits. This area of plant is However, a of a in the of gene expression that a gene for the role of epigenetics in gene the of to Performance 2. 1: Develop comprehensive gene expression data for soybean. of the soybean genome at genome sequencing all of the genes present within an it gene are for different or For example, and the yet their different are the of in gene is to the expression of genes on a sequencing a and to gene soybean gene expression are available and have to the expression of However, these resources to be expanded to many of or The of a soybean expression that all and be an for the of soybean gene improved soybean gene developed from which a comprehensive of all soybean and the of genes and gene data different and A for data the whole soybean genome Performance 2. 2: Develop near isogenic lines (NIL) to help reveal genetic mechanisms that mediate useful traits. The of the of functional genomic to soybean to yield and important that can be for soybean improvement. However, these can be in that and analysis. important is However, can be by the use of in that important traits. The of is for soybean to from the molecular Development of functional and breeders to identify traits. Develop of in the next to 5 yr for traits that Performance 2. 3: Develop an improved infrastructure to facilitate genome annotation. SoyBase is a comprehensive repository for genomics, and data resources for soybean. SoyBase contains and genomic sequence maps with qualitative and quantitative traits. SoyBase contains the genomic sequence and The genetic and sequence of soybean and the data on traits and are This to the database using of available as genetic map soybean gene or traits. SoyBase is the repository for for soybean and which are linked to the plant of the draft sequence of the soybean genome with gene with QTL marker However, the of these resources in breeding and other areas of on of data bioinformatic systems and in the practical of resources. improved gene that available data and other bioinformatic methods to data from existing sequence and expression for use in genome to genome and to A gene expression database with map with [verified 17 Mar. which integrates all of soybean functional data with the genome Performance 2. 4: Achieve high-definition genomic characterization of biological mechanisms and regulatory systems in soybean. analysis of gene is to of expression for plant and biological and improved of and transcription to the soybean genome Soybean to a of each with its of The biological of a of these is methods for soybean as and of soybean with an on methods as and approaches on soybean traits and and characterization of soybean in or in to and Performance 2. 5: Use functional genomic methods to characterize transcription regulated pathways. gene expression to biological is that for the of gene expression and can order of transcription these is to and to molecular methods to elucidate these their and A defined soybean regulatory by data with A defined soybean regulatory by expression and the and Performance 2. 6: Advance gene modification technologies to help associate candidate genes with a discrete phenotype. sequence gene may a function, it is to function or genetic studies. is that the function of the of soybean genes will be by sequence to other In these the of in each of the soybean genes will be useful to gene function and function the of soybean and For example, is a that the of and and in in the genome and is a to create can be used for both and genetic for useful A of technologies are to create for the of soybean gene for use of and technology as for gene function in soybean. from and a gene database for characterization of gene sequencing methods and other analytical to identify and characterize of to of resources. Performance 2. 7: Create a saturated transposon insertion population with defined flanking sequences that can be used to identify mutants by BLAST sequence comparison. as are in plant insertion has a high of gene a transposon in genetic analysis has that both the and are for use in soybean. The transposon has a for which it useful to genes in its and may be for However, a population of is soybean gene can be In the on all and in the of are available that may its of of the transposon for to of A population in which of soybean genes have methods for of lines. A comprehensive database of lines Performance 2. 8: Implement outreach opportunities for education and use of genomic databases. have be developed for soybean genome and databases. However, these are useful can be used and the of the to use and these will be an to the soybean and outreach to to an and and methods for data to resources. and expanded of for useful resources. across data within and species. that and databases. opportunities and to the community with database resources. database by and of existing Performance 2. 9: Develop an ORFeome library from agronomically important genes or and gene families. molecular methods developed to an function the of the gene of or its For example, a of the targeted be a targeting of it is to the gene or in a contains all the protein-coding of a the in that can molecular studies. A library of in facilitate of gene and a to the soybean Use of genes of to the of an Performance 3. 1: Establish a soybean genetic repository and distribution center. A repository for genetic resources is for and distribution of the germplasm that the soybean community The has of on soybean research for the development of and lines. This is in of being to the of an infrastructure for and The of a repository is critical to additional research In from be is to the developed with to genetic repository be with repository for additional and of a and to develop a plan and from and a and Determine and for and of existing resources the resources available with defined and Performance 3. 2: Develop next-generation transformation and targeting technologies and utilize these transgenic approaches to help elucidate gene function and deploy genes of interest. is to of the that are in in from of genes of to of gene these by the the of genes and gene technologies in the the of the soybean transformation to be an important for the soybean community to use to gene function as it to the genes of to soybean In addition, in gene and of as as of targeting are to germplasm to be by the soybean breeding and in gene expression and of of assembly for with of for targeted of to 5 Determine to gene for opportunities for use of transformation for characterization of gene of a to a transformation for genes of to the research of a for the process of gene and transgenic modification and insertion of genes that mediate productivity, or traits. Development of technologies for or Performance 4. 1: Develop analytical approaches to characterize soybean germplasm diversity based on the SoyHapMap 1. 0 data to identify parental lines for breeding purposes. The analysis of the entire USDA Soybean of soybean and soybean accessions with 50, 000 SNP DNA markers is the analysis of a germplasm collection. The development of SoyHapMap which will the of soybean genetic diversity based on the analysis of the and accessions in the USDA Soybean will be the for the discovery of genetic diversity and DNA marker resources. To the of approaches and are to the genomic data of A diversity map of in the USDA Soybean analysis to the of soybean haplotype diversity for genetic improvement. A Soybean with to with yield to all developed Performance 4. 2: Discover gene/QTL for qualitative traits and develop tightly linked DNA markers. are traits that are by or a genes and traits as pest resistance, and other and DNA markers that can be used in breeding for pest Phytophthora and other important DNA markers that can be used in breeding for traits and and other traits DNA markers for and selection for In the of the transgenic will a marker for the as as the genome Performance 4. 3: Discover gene/QTL for quantitative traits and develop tightly linked DNA markers. are traits that are by a number of and traits resistance, and DNA markers for that can be used to these yield DNA markers for under use and to of and DNA markers for yield QTL and Performance 4. 4: Develop and populate a user-friendly database of validated QTL for use in marker assisted breeding applications. The of breeding to data on for both and flanking markers with the in the qualitative or quantitative A defined and for QTL and data to database of and that QTL and data are to SoyBase for all Development of and to and breeders and on the use of SoyBase (see Performance Performance 4. 5: Define the molecular genetic signatures of selection in 70+ years of U. S. soybean breeding by use of the 50, 000 SNP Illumina Infinium Assay. by soybean breeders over the yr for yield and other traits has in at in the soybean genome. The of will the of genes these traits. of the at 50, 000 SNP loci based on analysis with the 50, 000 SNP Illumina Infinium of the and important used in elite breeding recently and the of in SNP over will information on which of the soybean genome have by of for important and traits. Performance 4. 6: Define optimum breeding models to different breeding situations using in silico analysis. To the from haplotype information and it is critical for soybean breeders to the optimum approaches to to create elite soybean cultivars. The of breeding models using in silico analysis will approaches to different breeding situations and soybean as the model for the of breeding of a have soybean that will in or To a model soybean the soybean community 20 plant important cultivars and to will be the soybean as a model crop for for the of a gene/QTL soybean based on for gene soybean based on for the of a soybean based on for the soybean genetic diversity based on A model soybean population for in silico studies. Institute for The of of North of United Soybean Board of of of of of of The of of of of United Soybean Board of of of Roger of Roger Boerma This workshop was by the United Soybean This was with the support of the United Soybean Board and to to for efforts in the meeting and workshop The meeting was by and the workshop it was be of last as Program with the United Soybean The of workshop to their to for and ability to issues and at the for the U. S. soybean a research was as a that the ability to technologies in to a of a of the soybean community to